Zobrazeno 1 - 10
of 1 038
pro vyhledávání: '"Lymberopoulos, A."'
This paper gives an overview of our ongoing work on the design space exploration of efficient deep neural networks (DNNs). Specifically, we cover two aspects: (1) static architecture design efficiency and (2) dynamic model execution efficiency. For s
Externí odkaz:
http://arxiv.org/abs/2011.10912
Autor:
Yu, Fuxun, Xu, Zirui, Shen, Tong, Stamoulis, Dimitrios, Shangguan, Longfei, Wang, Di, Madhok, Rishi, Zhao, Chunshui, Li, Xin, Karianakis, Nikolaos, Lymberopoulos, Dimitrios, Li, Ang, Liu, ChenChen, Chen, Yiran, Chen, Xiang
Despite the superb performance of State-Of-The-Art (SOTA) DNNs, the increasing computational cost makes them very challenging to meet real-time latency and accuracy requirements. Although DNN runtime latency is dictated by model property (e.g., archi
Externí odkaz:
http://arxiv.org/abs/2011.03897
Autor:
Ahmad, Aldelemy, Ebenezer, Adjei, Prince, Siaw, Buckley, John, Hardy, Maryann, Qahwaji, Rami S.R., Abd-Alhameed, Raed, Bastos, J., Barbosa, C., Elfergani, I., Lymberopoulos, D., Denazis, S., Mandellos, G., Martins, J., Campos, L., Loureiro, F., Monteiro, V.
Yes
This comprehensive review paper examines bone fracture detection techniques based on time-domain low-frequency and microwave radiofrequency (RF). Early and accurate diagnosis of bone fractures remains critical in healthcare, as it can signif
This comprehensive review paper examines bone fracture detection techniques based on time-domain low-frequency and microwave radiofrequency (RF). Early and accurate diagnosis of bone fractures remains critical in healthcare, as it can signif
Externí odkaz:
http://hdl.handle.net/10454/19722
Autor:
Yu, Fuxun, Wang, Di, Chen, Yinpeng, Karianakis, Nikolaos, Shen, Tong, Yu, Pei, Lymberopoulos, Dimitrios, Lu, Sidi, Shi, Weisong, Chen, Xiang
Current state-of-the-art object detectors can have significant performance drop when deployed in the wild due to domain gaps with training data. Unsupervised Domain Adaptation (UDA) is a promising approach to adapt models for new domains/environments
Externí odkaz:
http://arxiv.org/abs/1911.07158
Autor:
Bhatnagar, Anshul, Prakash, Sameer, Lymberopoulos, Peter, Goff, Cameron, Shaikh, Anjiya, Kim, Donghee, Ahmed, Aijaz, Berg, Carl, Naggie, Susanna, Kanwal, Fasiha, Cholankeril, George, Lee, Tzu-Hao
Publikováno v:
In American Journal of Transplantation August 2023 23(8):1221-1226
Autor:
Stamoulis, Dimitrios, Ding, Ruizhou, Wang, Di, Lymberopoulos, Dimitrios, Priyantha, Bodhi, Liu, Jie, Marculescu, Diana
Can we reduce the search cost of Neural Architecture Search (NAS) from days down to only few hours? NAS methods automate the design of Convolutional Networks (ConvNets) under hardware constraints and they have emerged as key components of AutoML fram
Externí odkaz:
http://arxiv.org/abs/1907.00959
We give a definition of isoclinic parametric surfaces in $\mathbb{R}^4_2$ and prove that such an isoclinic conformal immersion comes from two holomorphic functions. A Cauchy problem was proposed and solved, namely: construct an isoclinic and minimal
Externí odkaz:
http://arxiv.org/abs/1905.08629
Autor:
Stamoulis, Dimitrios, Ding, Ruizhou, Wang, Di, Lymberopoulos, Dimitrios, Priyantha, Bodhi, Liu, Jie, Marculescu, Diana
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the latency constraint of a mobile device? Neural Architecture Search (NAS) for ConvNet design is a challenging problem due to the comb
Externí odkaz:
http://arxiv.org/abs/1905.04159
In this work we provide necessary and sufficient conditions for the existence of a minimal timelike strip in Lorentz-Minkowski space $\mathbb{R}^4_1$ containing a given lightlike curve and prescribed normal bundle. We also discuss uniqueness of solut
Externí odkaz:
http://arxiv.org/abs/1905.01196
Autor:
Stamoulis, Dimitrios, Ding, Ruizhou, Wang, Di, Lymberopoulos, Dimitrios, Priyantha, Bodhi, Liu, Jie, Marculescu, Diana
Can we automatically design a Convolutional Network (ConvNet) with the highest image classification accuracy under the runtime constraint of a mobile device? Neural architecture search (NAS) has revolutionized the design of hardware-efficient ConvNet
Externí odkaz:
http://arxiv.org/abs/1904.02877